【摘要】 为探索空谱融合方法在融合无人机影像与Sentinel-2影像方面的潜力，选取了Gram-Schmidt Adaptive(GSA)、双尺度引导图像滤波器（dual-scale guided image filter, DGIF）、基于平滑滤波器的强度调制（Smoothing Filter-based Intensify Modulation, SFIM）、区域对点回归克里金（Area-To-Point Regression Kriging, ATPRK）和耦合非负矩阵分解（Coupled Nonnegative Matrix Factorization, CNMF）5种覆盖多类别的空谱融合方法进行试验验证。通过定量与定性相结合的分析手段，全面评估了融合后影像在光谱保真度与空间细节保持方面的表现，并深入探讨了空谱融合方法对于不同空间尺度差异影像的适应性。试验结果表明，随着影像间空间尺度差异的增大，融合精度普遍呈现下降趋势，但其中GSA方法展现出相对较好的稳健性与融合效果。特别是在融合空间尺度差异较大的无人机与Sentinel-2影像时，GSA方法表现出较高的鲁棒性。更多还原

【Abstract】 The potential of spatial-spectral fusion methods in integrating UAV imagery and Sentinel-2 imagery was studied with Gram-Schmidt Adaptive(GSA), dual-scale guided image filter(DGIF), Smoothing Filter-based Intensify Modulation(SFIM),Area-To-Point Regression Kriging(ATPRK), and Coupled Nonnegative Matrix Factorization(CNMF). The spectral fidelity and spatial detail preservation of the fused imagery were evaluated with quantitative analysis and qualitative analysis, and the adaptability of spatial-spectral fusion methods to imagery with different spatial scale differences was discussed. The results showed that the fusion accuracy generally decreased as the spatial scale difference between the images increased, while the GSA method demonstrated better robustness and fusion effectiveness, especially in fusing UAV and Sentinel-2 imagery with large spatial scale differences.更多还原